{"id":"W2157880479","doi":"10.1145/2576768.2598303","title":"Automatic design of sound synthesizers as pure data patches using coevolutionary mixed-typed cartesian genetic programming","year":2014,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Council for the Arts","keywords":"Computer science; Set (abstract data type); Mel-frequency cepstrum; Genetic algorithm; Population; Cepstrum; Fitness function; Genetic programming; Noise (video); Speech recognition; Artificial intelligence; Programming language; Machine learning; Feature extraction","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006282692,0.0005400466,0.0004577695,0.0004490123,0.0002482471,0.0007614648,0.001020221,0.0005992665,0.001751504],"category_scores_gemma":[0.001811284,0.0003790467,0.0006071879,0.0002864499,0.0006914209,0.0005385294,0.0008178591,0.0006271806,0.0002711378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004114975,"about_ca_system_score_gemma":0.0004715679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001027056,"about_ca_topic_score_gemma":0.001031337,"domain_scores_codex":[0.9997,0.00008412345,0.00001804396,0.00008455279,0.00008056612,0.00003284108],"domain_scores_gemma":[0.9995354,0.0002475072,0.00003852761,0.00005528746,0.0000968489,0.00002646505],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001570175,0.0001119002,0.002245251,0.0001752912,0.00006573395,0.0002451439,0.000474824,0.7202724,0.07980302,0.03411999,0.0007274874,0.1616019],"study_design_scores_gemma":[0.00002323597,0.0000602986,0.0001342819,0.000009147381,0.00001904548,0.00005035193,0.0000329916,0.9866115,0.006537529,0.00498791,0.001527602,0.000006098003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04600836,0.00006340382,0.9514667,0.00005102186,0.00001759858,0.00006205817,0.00001417246,0.0005012958,0.001815407],"genre_scores_gemma":[0.3642842,0.00009958849,0.6328879,0.00006593682,0.000008530738,0.0002539209,0.00005989627,0.0001775113,0.00216254],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001751504,"threshold_uncertainty_score":0.005859375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04763265770930345,"score_gpt":0.2759694271392312,"score_spread":0.2283367694299277,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}